{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for your own interest.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Done extracting features for 49000 / 49000 images\n"
     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 10 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-09 reg 5.000000e+04 train accuracy: 0.090714 val accuracy: 0.093000\n",
      "lr 1.000000e-09 reg 5.000000e+05 train accuracy: 0.111184 val accuracy: 0.101000\n",
      "lr 1.000000e-09 reg 5.000000e+06 train accuracy: 0.413245 val accuracy: 0.414000\n",
      "lr 1.000000e-08 reg 5.000000e+04 train accuracy: 0.106082 val accuracy: 0.092000\n",
      "lr 1.000000e-08 reg 5.000000e+05 train accuracy: 0.416102 val accuracy: 0.417000\n",
      "lr 1.000000e-08 reg 5.000000e+06 train accuracy: 0.412061 val accuracy: 0.421000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.413184 val accuracy: 0.414000\n",
      "lr 1.000000e-07 reg 5.000000e+05 train accuracy: 0.410347 val accuracy: 0.403000\n",
      "lr 1.000000e-07 reg 5.000000e+06 train accuracy: 0.336694 val accuracy: 0.323000\n",
      "best validation accuracy achieved during cross-validation: 0.421000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9, 1e-8, 1e-7]\n",
    "regularization_strengths = [5e4, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for learning_rate in learning_rates:\n",
    "    for regularization_strength in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        loss_hist = svm.train(X_train_feats, y_train, learning_rate=learning_rate, \n",
    "                              reg=regularization_strength, num_iters=1500, verbose=False)\n",
    "        y_train_pred = svm.predict(X_train_feats)\n",
    "        training_accuracy = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = svm.predict(X_val_feats)\n",
    "        validation_accuracy = np.mean(y_val == y_val_pred)\n",
    "        results[(learning_rate, regularization_strength)] = (training_accuracy, validation_accuracy)\n",
    "        if best_val < validation_accuracy:\n",
    "            best_val = validation_accuracy\n",
    "            best_svm = svm\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.427\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 80 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "\n",
    "examples_per_class = 8\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense?\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n",
      "(49000, 154)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: Remove the bias dimension\n",
    "# Make sure to run this cell only ONCE\n",
    "print(X_train_feats.shape)\n",
    "X_train_feats = X_train_feats[:, :-1]\n",
    "X_val_feats = X_val_feats[:, :-1]\n",
    "X_test_feats = X_test_feats[:, :-1]\n",
    "\n",
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_dim = X_train_feats.shape[1]\n",
    "hidden_dim = 500\n",
    "num_classes = 10\n",
    "\n",
    "net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "best_net = None\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "learning_rates = [1e-3, 5e-4]\n",
    "regularization_strengths = [0.25, 0.5]\n",
    "\n",
    "for learning_rate in learning_rates:\n",
    "    for regularization_strength in regularization_strengths:\n",
    "        net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "        loss_hist = net.train(X_train_feats, y_train, X_val_feats, y_val,\n",
    "            num_iters=1000, batch_size=200,\n",
    "            learning_rate=learning_rate, learning_rate_decay=0.95,\n",
    "            reg=regularization_strength, verbose=False)\n",
    "        y_train_pred = net.predict(X_train_feats)\n",
    "        training_accuracy = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = net.predict(X_val_feats)\n",
    "        validation_accuracy = np.mean(y_val == y_val_pred)\n",
    "        results[(learning_rate, regularization_strength)] = (training_accuracy, validation_accuracy)\n",
    "        if best_val < validation_accuracy:\n",
    "            best_val = validation_accuracy\n",
    "            best_net = net\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.102\n"
     ]
    }
   ],
   "source": [
    "# Run your best neural net classifier on the test set. You should be able\n",
    "# to get more than 55% accuracy.\n",
    "\n",
    "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  }
 ],
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